scholarly journals Cosmic shear measurement with maximum likelihood and maximum a posteriori inference

2017 ◽  
Vol 468 (1) ◽  
pp. 346-363 ◽  
Author(s):  
Alex Hall ◽  
Andy Taylor
Keyword(s):  
Maximum Likelihood ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Cosmic Shear

scholarly journals Score-Based Measurement Invariance Checks for Bayesian Maximum-a-Posteriori Estimates in Item Response Theory

2020 ◽  
Author(s):  
Rudolf Debelak ◽  
Samuel Pawel ◽  
Carolin Strobl ◽  
Edgar C. Merkle
Keyword(s):  
Maximum Likelihood ◽  
Item Response Theory ◽  
Item Response ◽  
Statistical Tests ◽  
Maximum A Posteriori ◽  
Model Parameters ◽  
A Posteriori ◽  
Response Theory ◽  
Simulation Based ◽  
A Posteriori Estimates

A family of score-based tests has been proposed in the past years for assessing the invariance of model parameters in several models of item response theory. These tests were originally developed in a maximum likelihood framework. This study aims to extend the theoretical framework of these tests to Bayesian maximum-a-posteriori estimates and to multiple group IRT models. We propose two families of statistical tests, which are based on a) an approximation using a pooled variance method, or b) a simulation-based approach based on asymptotic results. The resulting tests were evaluated by a simulation study, which investigated their sensitivity against differential item functioning with respect to a categorical or continuous person covariate in the two- and three-parametric logistic models. Whereas the method based on pooled variance was found to be practically useful with maximum likelihood as well as maximum-a-posteriori estimates, the simulation-based approach was found to require large sample sizes to lead to satisfactory results.

A method for approximating the density of maximum-likelihood and maximum a posteriori estimates under a Gaussian noise model

1998 ◽  
Vol 2 (4) ◽  
pp. 395-403 ◽  
Author(s):  
Craig K. Abbey ◽  
Eric Clarkson ◽  
Harrison H. Barrett ◽  
Stefan P. Müller ◽  
Frank J. Rybicki
Keyword(s):  
Maximum Likelihood ◽  
Gaussian Noise ◽  
Noise Model ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
A Posteriori Estimates

scholarly journals Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

2017 ◽  
Author(s):  
Francisco Gomariz-Castillo ◽  
Francisco Alonso-Sarría ◽  
Fulgencio Cánovas-García
Keyword(s):  
Maximum Likelihood ◽  
Random Forest ◽  
Classification Accuracy ◽  
Maximum A Posteriori ◽  
Support Vector ◽  
Textural Features ◽  
A Posteriori ◽  
Semiarid Area ◽  
Vector Machines ◽  
Terrain Features

The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectral and textural features, and spectral, textural and terrain features; and iii) using different image-sets: winter, spring, summer, autumn, winter+summer, winter+ spring+summer; and a four seasons combination. A 3-way ANOVA is used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM, and OLI images corresponding to the period 2000-2015. A combination of four images was the best way to improve accuracy. Maximum Likelihood, Random Forest and Support Vector Machines do not significantly increase accuracy when textural information is added, but do so when terrain features are taken into account. On the other hand, Sequential Maximum a Posteriori increases accuracy when textural features are used, but reduces accuracy substantially when terrain features are included. Random Forest using the three feature subsets and Sequential Maximum a Posteriori with spectral and textural features had the largest kappa values, around 0.9.

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